Social Media Zero-Day Attack Detection Using TensorFlow

• DOI: 10.3390/electronics12173554
• Publication Date: 2023-08-23T12:01:21Z
• Authors: Ahmet Ercan Topcu; Yehia Ibrahim Alz...; Ersin Elbasi; Emre Camalan
• Keywords: Realm

In the current information era, knowledge can pose risks in online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types of referred zero-day attacks since no pre-existing anti-malware measures available mitigate them. Consequently, significant damages occur when vulnerabilities systems exploited. The effectiveness security systems, such IPS IDS, relies heavily on prompt efficient response threats. Failure address these issues promptly hinders system developers. purpose this study analyze data from Twitter platform deploy machine learning techniques, word categorization, identify counteract swiftly. TensorFlow was utilized handle processing conversion raw data, resulting efficiency improvements. Moreover, we integrated Natural Language Toolkit (NLTK) tool extract targeted words various languages. Our results indicate that achieved an 80% success rate detecting by using our tool. By utilizing publicly shared individuals, relevant providers informed. This approach enables companies patch more quickly.